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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers.

Pandikannan Krishnamoorthy¹, Athira S Raj¹, Himanshu Kumar^1,2

¹Laboratory of Immunology and Infectious Disease Biology, Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Bhopal, Bhopal, Madhya Pradesh, India.

Journal of Medical Virology

|January 10, 2023

Summary

A new 7-gene biomarker accurately distinguishes COVID-19 from other respiratory illnesses and predicts disease severity. This discovery offers potential for early diagnosis and reduced mortality from Coronavirus disease 2019 (COVID-19).

Keywords:

SARS-CoV-2 blood biomarker machine learning meta-analysis transcriptome

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Area of Science:

Biotechnology
Bioinformatics
Genomics

Background:

The Coronavirus disease 2019 (COVID-19) pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, remains a global health crisis.
Differentiating COVID-19 from other respiratory infections is challenging due to overlapping symptoms, necessitating improved diagnostic tools.
Early identification of biomarkers is crucial for predicting disease severity and mitigating mortality during outbreaks.

Purpose of the Study:

To develop a novel diagnostic and prognostic tool for COVID-19 using integrated bioinformatics and machine learning.
To identify a robust gene signature capable of distinguishing SARS-CoV-2 associated acute respiratory illness (ARI) from other ARIs.
To validate the identified biomarker's ability to differentiate between severe and non-severe COVID-19 cases and assess its prognostic value.

Main Methods:

Integration of bioinformatics and machine learning algorithms applied to publicly available COVID-19 transcriptome datasets.
Identification and validation of a 7-gene biomarker panel.
Analysis of an independent blood transcriptome dataset for longitudinal assessment of COVID-19 patients.

Main Results:

A robust 7-gene biomarker was identified, capable of discriminating SARS-CoV-2 associated ARI from other ARIs.
The biomarker successfully differentiated severe COVID-19 patients from non-severe cases.
Validation in an independent dataset confirmed the biomarker's dysregulation in severe disease, with restoration during recovery, indicating prognostic potential.

Conclusions:

The identified 7-gene blood biomarker holds significant potential for the early diagnosis of COVID-19.
This biomarker may aid in predicting disease severity and reducing COVID-19 associated mortality.
The findings support the use of this biomarker as a candidate diagnostic and prognostic tool in clinical settings.